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Update base.py #333

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Update base.py
jzakirov Jan 27, 2023
cc75629
Merge branch 'master' into compute_grads_fix
denproc Feb 7, 2023
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18 changes: 14 additions & 4 deletions piq/functional/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,17 +49,27 @@ def similarity_map(map_x: torch.Tensor, map_y: torch.Tensor, constant: float, al

def gradient_map(x: torch.Tensor, kernels: torch.Tensor) -> torch.Tensor:
r""" Compute gradient map for a given tensor and stack of kernels.

Args:
x: Tensor with shape (N, C, H, W).
kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W)
kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W) or (k_N, 1, k_H, k_W)

@denproc denproc Feb 8, 2023

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Suggested change
kernels: Stack of tensors for gradient computation with shape (k_N, k_H, k_W) or (k_N, 1, k_H, k_W)
kernels: Stack of tensors for gradient computation with shape (k_C_out, k_C_in, k_H, k_W). k_C_in equals 1.

Returns:
Gradients of x per-channel with shape (N, C, H, W)
"""
padding = kernels.size(-1) // 2
grads = torch.nn.functional.conv2d(x, kernels, padding=padding)
N, C, H, W = x.shape

# Expand kernel if this is not done already and repeat to match number of groups
if kernels.dim() != 4:
kernels = kernels.unsqueeze(1)

Comment on lines 58 to +64

@denproc denproc Feb 8, 2023

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padding = kernels.size(-1) // 2
grads = torch.nn.functional.conv2d(x, kernels, padding=padding)
N, C, H, W = x.shape
# Expand kernel if this is not done already and repeat to match number of groups
if kernels.dim() != 4:
kernels = kernels.unsqueeze(1)
assert kernels.dim() == 4, f'Expected 4D kernel, got {kernels.dim()}D tensor '
assert kernels.size(1) == 1, f'Expected dimension size of kernel to be equal one for input number of channels, got kernel {kernel.size()} '
assert kernels.size(-1) == kernel_size(-2), f'Expected squared kernel along coast two dimensions, got {kernel.size()}'
padding = kernels.size(-1) // 2
N, C, H, W = x.shape

if C > 1:
kernels = kernels.repeat(C, 1, 1, 1)

# Process each channel separately using group convolution.
grads = torch.nn.functional.conv2d(x, kernels.to(x), groups=C, padding=padding)

@denproc denproc Feb 7, 2023 •
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Implicit transfer between devices is not needed as current implementation in master handles the device and dtype properties.

Suggested change
grads = torch.nn.functional.conv2d(x, kernels.to(x), groups=C, padding=padding)
grads = torch.nn.functional.conv2d(x, kernels, groups=C, padding=padding)


return torch.sqrt(torch.sum(grads ** 2, dim=-3, keepdim=True))
# Create a per-channel view, compute square of grads and return
return torch.sqrt(torch.sum(grads.view(N, C, -1, H, W) ** 2, dim=-3))


def pow_for_complex(base: torch.Tensor, exp: Union[int, float]) -> torch.Tensor:
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